关键词: 社会网络, 隐私保护, 分布式, k-度-m-标签匿名, GraphX

Abstract: Existing anonymous technologies pay more attention to the utility of anonymous data, ignoring the problem that attackers recognize targets through a variety of background knowledge. In addition, with the scale of users increasing year by year, the traditional anonymous technologies can??t meet the actual demand. Motivated by this, a distributed k-degree-m-label anonymity method with protecting link relationships (called PLRD-(k, m)) is proposed. This method utilizes the message passing mechanism of GraphX, and divides nodes that are N-hop neighbors into one group and performs k-degree anonymous and m-label anonymous to ensure that attackers can??t identify targets by degrees and labels, and protects link relationships from being leaked. Finally, a protecting link relationships distributed personalized anonymity method extended from PLRD-(k, m) is proposed. Experimental results based on real social network datasets show that the proposed method can not only improve the execution efficiency of large-scale social networks, but also has good data utility.

Key words: social network, privacy preserving, distributed, k-degree-m-label anonymity, GraphX